import torch
import numpy as np
from clustering import AS_DBSCAN
from pruning import dynamic_pruning
from knowledge_distillation import aggregate_knowledge
from utils import calculate_jsd

class Server:
    def __init__(self, config):
        self.config = config
        self.global_model = self.initialize_global_model()
        self.client_metrics = {}
        self.clusters = []
        
    def initialize_global_model(self):
        # 实现基础模型结构（参考论文3.2.1节）
        model = torch.nn.Sequential(
            torch.nn.Conv1d(1, 32, kernel_size=5),
            torch.nn.ReLU(),
            torch.nn.MaxPool1d(2),
            torch.nn.Flatten(),
            torch.nn.Linear(32*38, 128),
            torch.nn.ReLU(),
            torch.nn.Linear(128, 2)
        )
        return model
    
    def aggregate(self, clients):
        # 收集交换模型和性能指标
        exchange_models = []
        performance_metrics = []
        
        for client in clients:
            exchange_models.append(client.exchange_model.state_dict())
            self.client_metrics[client.client_id] = {
                'accuracy': client.accuracy_history[-1],
                'stability': client.calculate_stability()
            }
        
        # 动态剪枝（Algorithm 1）
        selected_clients = dynamic_pruning(
            exchange_models, 
            self.client_metrics, 
            self.config['pruning_threshold']
        )
        
        # 知识聚合（3.2.1节）
        aggregated_knowledge = aggregate_knowledge(
            [clients[i] for i in selected_clients],
            self.config
        )
        
        # 更新全局模型
        self.update_global_model(aggregated_knowledge)
    
    def optimize_clusters(self):
        # AS-DBSCAN聚类（Algorithm 2）
        features = self.extract_client_features()
        self.clusters = AS_DBSCAN(
            features,
            self.config['dbscan_eps'],
            self.config['dbscan_min_samples']
        )
    
    def extract_client_features(self):
        # 提取客户端特征用于聚类（预测分布）
        features = []
        for client_id, metrics in self.client_metrics.items():
            features.append(metrics['prediction_distribution'])
        return np.array(features)
    
    def distribute_models(self, clients):
        # 分发全局模型和聚类信息
        for cluster_id, cluster_members in enumerate(self.clusters):
            for client_id in cluster_members:
                clients[client_id].receive_global_model(
                    self.global_model.state_dict(),
                    cluster_id
                )
    
    def evaluate(self, clients):
        # 评估最终性能
        total_accuracy = 0
        for client in clients:
            client.evaluate()
            total_accuracy += client.accuracy_history[-1]
        
        avg_accuracy = total_accuracy / len(clients)
        print(f"Final Average Accuracy: {avg_accuracy:.4f}")

    def update_global_model(self, aggregated_knowledge):
        # 实现模型更新逻辑（FedAvg变体）
        pass
